Electronic communication has become a pervasive means of communicating between individuals at remote locations. Email services, short message service (SMS) messaging, chat applications, social media applications, and numerous other services have revolutionized that way individuals and organizations can communicate with each other and one another. However, the rise of electronic communication has also generated new paradigms in conversation. For example, communication over an email, SMS, or chat applications is far different from in-person communication or even telephone conversations. Indeed, a transcribed telephone conversation or in-person conversation would look far different from an email, SMS, or chat transcription about the same topic or subject matter. The subtleties of person to person interactions, such as tone, emotional queues, and reactions to other's communication, are expressed differently in electronic communication.
In some instances, these subtleties are expressed differently among electronic communication itself. For example, an email may be drafted with a level of formality that a chat message or SMS message lacks, and thus the manner in which tone or emotional queues are communicated may be different. The ability to understand and identify emotional queues or reactions within electronic communication has become both immensely important for many business, such as service industries or the service component to a business selling a product, and a vexing issue to solve. For example, current customer service divisions may be dependent on individuals to detect a customer's emotions or reaction, such as dissatisfaction at the experience or anger at the product or service that the interaction relates to, and thus the quality of the customer service may vary greatly based on the individual providing the service. In other examples, marketing divisions that are better able to detect excitement over a product or service in electronic communication will have more success converting potential customers into realized customers.
Thus, there is a need to improve the detection techniques for an individual's reactions or emotions in electronic communication. In particular, an increase in the reliability of such detection can improve the consistency for services offered, such as customer service services or marketing services. One technique for realizing such an improvement relies on a technological improvement in the way such electronic communication is analyzed.
For example, artificial intelligence techniques have helped industries leverage computing systems to make various improvements, from quality control to cost efficiency. However, artificial intelligence systems can have varying degrees of success. A computing system's ability to learn is highly dependent on the circumstances presented to it. In other words, artificial intelligence systems' rely on well-tailored technological solutions to provide quality service.
When considering an artificial intelligence system's ability to reliably predict emotion or reactions in electronic communication, a number of potential solutions are available. Thus, the application of the technology plays a pivotal role in the effectiveness of any solution. Bulky or overly complex solutions may pose a number of problems when these solutions are paired with artificial intelligence systems, as the complexity may introduce problems that in turn fail to ensure reliable and accurate results. There exists a need to provide simplistic yet effective solutions that leverage artificial intelligence systems to provide reliable and accurate prediction of emotion or reaction in electronic communication.